def test_data(self):
dataset = ActionEncoderDataset(
config.datapath('SpaceInvaders-v4/latent'))
devset = data_utils.Subset(dataset, range(1))
dev = data_utils.DataLoader(devset,
batch_size=1,
collate_fn=collate_action_observation)
for raw_latent, first_frame, delta_latent, action_minibatch in dev:
assert raw_latent[0][0, 0,
0] - first_frame[0][0, 0,
0] == delta_latent[0][0,
0,
0]
ds = data.DeltaStream(first_frame[0])
zero_delta = torch.zeros(first_frame[0].shape)
assert ds.delta_to_frame(zero_delta).sum() == first_frame[0].sum()
for i, delta in enumerate(delta_latent[0]):
from_delta = ds.delta_to_frame(
delta_latent[0][i, :, :])[0, 0, 0].item()
original = raw_latent[0][i, 0, 0].item()
print('test ' + str(i))
if from_delta != original:
print('delta %.15f' % delta_latent[0][i, 0, 0].item())
print('frame from delta %.15f' % from_delta)
print('original %.15f' % original)
import math
assert math.isclose(from_delta,
original,
rel_tol=1e-09,
abs_tol=1e-05)
from torch.optim import Adam
if __name__ == '__main__':
input_viewer = ImageViewer('input', (320, 480))
output_viewer = ImageViewer('output', (320, 480))
latent_viewer = ImageViewer('latent', (320, 480))
latent_instr = LatentInstrument()
invader = tf.ColorMask(lower=[120, 125, 30],
upper=[140, 140, 130],
append=False)
cut_invader = tf.SetRange(0, 30, 0, 210, [0])
co_ord_conv_shots = GymImageDataset(
directory=config.datapath(r'SpaceInvaders-v4\images\raw_v1\all'),
input_transform=TVT.Compose(
[invader, cut_invader,
TVT.ToTensor(),
tf.CoordConv()]),
target_transform=TVT.Compose([invader, cut_invader,
TVT.ToTensor()]))
co_ord_conv_data_package = DataPackage(co_ord_conv_shots, StandardSelect())
run_fac = SimpleRunFac()
compressor = Params(Compressor, (210, 160),
1,
input_channels=3,
output_channels=1)
return batch
if __name__ == '__main__':
batch_size = 128
i_size = 16 + 6
z_size = 16
train_model = True
config.increment('run_id')
clamp = False
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataset = ActionEncoderDataset(
config.datapath('SpaceInvaders-v4/rl_raw_v2'),
load_observation=False,
load_screen=False)
dataset = RandomSubSequence(dataset, 100)
dev = data_utils.Subset(dataset, range(len(dataset) * 2 // 10))
train = data_utils.Subset(dataset, range(0, len(dataset) * 9 // 10))
test = data_utils.Subset(dataset,
range(len(dataset) * 9 // 10 + 1, len(dataset)))
dev = data_utils.DataLoader(
dev,
batch_size=batch_size,
collate_fn=collate_action_observation,
drop_last=False,
)
decode_viewer.update(image)
decode2_viewer.update(image)
shots = tf.ColorMask(lower=[128, 128, 128], upper=[255, 255, 255], append=True)
player = tf.ColorMask(lower=[30, 100, 40], upper=[70, 180, 70], append=True)
cut_player = tf.SetRange(0, 60, 0, 210, [4])
invader = tf.ColorMask(lower=[120, 125, 30], upper=[140, 140, 130], append=True)
cut_invader = tf.SetRange(0, 30, 0, 210, [5])
barrier = tf.ColorMask(lower=[120, 74, 30], upper=[190, 100, 70], append=True)
select = tf.SelectChannels([3, 4, 5, 6])
segmentor = TVT.Compose([shots, player, cut_player, invader, cut_invader,
barrier, select, TVT.ToTensor(), tf.CoordConv()])
co_ord_conv_shots = GymImageDataset(directory=config.datapath(r'SpaceInvaders-v4\images\raw_v1\all'),
input_transform=segmentor,
target_transform=segmentor)
co_ord_conv_data_package = DataPackage(co_ord_conv_shots, StandardSelect())
channel_coder = Params(MultiChannelAE)
opt = Params(Adam, lr=1e-3)
run_fac = SimpleRunFac(increment_run=False)
run_fac.run_list.append(Run(channel_coder, None, None, co_ord_conv_data_package,
run_name='shots_v1', trainer=SimpleInference()))
#run_fac = SimpleRunFac.resume(r'C:\data\runs\549', co_ord_conv_data_package)
batch_size = 1
epochs = 30